Spread Analysis (COIN/BTC)The Spread Analysis (COIN/BTC) indicator calculates the Z-score of the price ratio between Coinbase stock ( NASDAQ:COIN ) and Bitcoin ( CRYPTOCAP:BTC ). It helps identify overbought or oversold conditions based on deviations from the historical mean of their price relationship.
Key Features:
Z-Score Calculation:
• Tracks the relative price ratio of NASDAQ:COIN to $BTC.
• Compares the current ratio to its historical average, highlighting extreme overvaluation or undervaluation.
• Buy and Sell Signals:
• Buy Signal: Triggered when the Z-score is less than -2, indicating NASDAQ:COIN may be undervalued relative to $BTC.
• Sell Signal: Triggered when the Z-score exceeds 2, suggesting NASDAQ:COIN may be overvalued relative to $BTC.
• Dynamic Z-Score Visualization:
• Blue line plots the Z-score over time.
• Dashed lines at +2 and -2 mark overbought and oversold thresholds.
• Green and red triangles highlight actionable buy and sell signals.
Use Case:
This indicator is ideal for identifying relative valuation opportunities between NASDAQ:COIN and $BTC. Use it to exploit divergences in their historical relationship and anticipate potential reversions to the mean.
Limitations:
• Best suited for range-bound markets; may produce false signals in strongly trending conditions.
• Assumes a consistent correlation between NASDAQ:COIN and CRYPTOCAP:BTC , which may break during independent price drivers like news or earnings.
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IU EMA Channel StrategyIU EMA Channel Strategy
Overview:
The IU EMA Channel Strategy is a simple yet effective trend-following strategy that uses two Exponential Moving Averages (EMAs) based on the high and low prices. It provides clear entry and exit signals by identifying price crossovers relative to the EMAs while incorporating a built-in Risk-to-Reward Ratio (RTR) for effective risk management.
Inputs ( Settings ):
- RTR (Risk-to-Reward Ratio): Define the ratio for risk-to-reward (default = 2).
- EMA Length: Adjust the length of the EMA channels (default = 100).
How the Strategy Works
1. EMA Channels:
- High-based EMA: EMA calculated on the high price.
- Low-based EMA: EMA calculated on the low price.
The area between these two EMAs creates a "channel" that visually highlights potential support and resistance zones.
2. Entry Rules:
- Long Entry: When the price closes above the high-based EMA (crossover).
- Short Entry: When the price closes below the low-based EMA (crossunder).
These entries ensure trades are taken in the direction of momentum.
3. Stop Loss (SL) and Take Profit (TP):
- Stop Loss:
- For long positions, the SL is set at the previous bar's low.
- For short positions, the SL is set at the previous bar's high.
- Take Profit:
- TP is automatically calculated using the Risk-to-Reward Ratio (RTR) you define.
- Example: If RTR = 2, the TP will be 2x the risk distance.
4. Exit Rules:
- Positions are closed at either the stop loss or the take profit level.
- The strategy manages exits automatically to enforce disciplined risk management.
Visual Features
1. EMA Channels:
- The high and low EMAs are dynamically color-coded:
- Green: Price is above the EMA (bullish condition).
- Red: Price is below the EMA (bearish condition).
- The area between the EMAs is shaded for better visual clarity.
2. Stop Loss and Take Profit Zones:
- SL and TP levels are plotted for both long and short positions.
- Zones are filled with:
- Red: Stop Loss area.
- Green: Take Profit area.
Be sure to manage your risk and position size properly.
DAILY Supertrend + EMA Crossover with RSI FilterThis strategy is a technical trading approach that combines multiple indicators—Supertrend, Exponential Moving Averages (EMAs), and the Relative Strength Index (RSI)—to identify and manage trades.
Core Components:
1. Exponential Moving Averages (EMAs):
Two EMAs, one with a shorter period (fast) and one with a longer period (slow), are calculated. The idea is to spot when the faster EMA crosses above or below the slower EMA. A fast EMA crossing above the slow EMA often suggests upward momentum, while crossing below suggests downward momentum.
2. Supertrend Indicator:
The Supertrend uses Average True Range (ATR) to establish dynamic support and resistance lines. These lines shift above or below price depending on the prevailing trend. When price is above the Supertrend line, the trend is considered bullish; when below, it’s considered bearish. This helps ensure that the strategy trades only in the direction of the overall trend rather than against it.
3. RSI Filter:
The RSI measures momentum. It helps avoid buying into markets that are already overbought or selling into markets that are oversold. For example, when going long (buying), the strategy only proceeds if the RSI is not too high, and when going short (selling), it only proceeds if the RSI is not too low. This filter is meant to improve the quality of the trades by reducing the chance of entering right before a reversal.
4. Time Filters:
The strategy only triggers entries during user-specified date and time ranges. This is useful if one wants to limit trading activity to certain trading sessions or periods with higher market liquidity.
5. Risk Management via ATR-based Stops and Targets:
Both stop loss and take profit levels are set as multiples of the ATR. ATR measures volatility, so when volatility is higher, both stops and profit targets adjust to give the trade more breathing room. Conversely, when volatility is low, stops and targets tighten. This dynamic approach helps maintain consistent risk management regardless of market conditions.
Overall Logic Flow:
- First, the market conditions are analyzed through EMAs, Supertrend, and RSI.
- When a buy (long) condition is met—meaning the fast EMA crosses above the slow EMA, the trend is bullish according to Supertrend, and RSI is below the specified “overbought” threshold—the strategy initiates or adds to a long position.
- Similarly, when a sell (short) condition is met—meaning the fast EMA crosses below the slow EMA, the trend is bearish, and RSI is above the specified “oversold” threshold—it initiates or adds to a short position.
- Each position is protected by an automatically calculated stop loss and a take profit level based on ATR multiples.
Intended Result:
By blending trend detection, momentum filtering, and volatility-adjusted risk management, the strategy aims to capture moves in the primary trend direction while avoiding entries at excessively stretched prices. Allowing multiple entries can potentially amplify gains in strong trends but also increases exposure, which traders should consider in their risk management approach.
In essence, this strategy tries to ride established trends as indicated by the Supertrend and EMAs, filter out poor-quality entries using RSI, and dynamically manage trade risk through ATR-based stops and targets.
Volume and Price, EMA Hierarchy Scoring Relations V 1.1Understanding the Volume and Price, EMA Hierarchy Scoring Indicator
Financial markets are often analyzed through a series of technical indicators, each providing valuable but isolated insights into price movements, volume dynamics, and trends. While these tools are widely used, they often lack context when applied individually. The Volume and Price, EMA Hierarchy Scoring Indicator was developed to bridge this gap by introducing structure, context, and relationships between these known indicators.
By utilizing Exponential Moving Averages (EMAs) and assigning periods derived from prime numbers, this indicator creates a scoring system that evaluates the relative positioning and interaction of 13 widely used technical tools. This approach adds meaning to individual indicator outputs by:
Revealing how their results align, diverge, or complement each other.
Quantifying their collective behavior through a hierarchy scoring system.
Enabling traders to not only analyze indicators individually but also combine them to uncover how they influence and interact with each other.
The result is a tool that provides clarity and insight into market behavior, enabling traders to move beyond surface-level analysis and uncover deeper patterns and relationships within the data.
Key Features and Methodology
The Volume and Price, EMA Hierarchy Scoring Indicator is built on a robust mathematical framework that evaluates and visualizes the relationships between 13 widely used technical indicators. By leveraging Exponential Moving Averages (EMAs) and prime numbers, the indicator provides meaningful insights into individual indicator performance as well as their combined behavior.
1. EMA Hierarchy Scoring
At the core of the indicator is its ability to assess the hierarchy of EMAs for each tool. This hierarchy scoring evaluates how the EMAs are aligned relative to one another, providing traders with a quantifiable measure of the indicator's internal consistency and its alignment with trends.
How It Works:
Each EMA is assigned a period derived from a unique prime number. This ensures that no two EMAs overlap, preserving their individuality.
The scoring system measures the gaps between these EMAs, assigning weighted values to these relationships based on their position in the hierarchy.
Why Prime Numbers?
Prime numbers ensure that the EMA periods are distinct and mathematically unrelated, creating a structured yet diverse dataset for analysis.
This approach allows the scoring system to capture both short-term and long-term trends, while avoiding redundancy.
2. Independent Indicator Evaluation
One of the key features of this indicator is the ability to analyze any of the 13 tools individually. Each indicator has its own module, complete with adjustable parameters and dedicated visualizations:
Histograms: Represent the raw EMA hierarchy score. Positive bars indicate alignment with upward trends, while negative bars highlight potential reversals or misalignments.
Smoothed Line: Averages the histogram values, reducing short-term noise and emphasizing longer-term trends.
Signal Line: Highlights trend shifts by smoothing the smoothed line further. Crossovers between the smoothed line and the signal line act as actionable signals for traders.
3. Combining Indicators for Context
Beyond individual analysis, the indicator allows users to combine multiple indicators to evaluate their interactions. For example:
Pairing ALMA (price smoothing) with Volume enables traders to see how price trends are supported or contradicted by market activity.
Combining Delta Volume and CMF (Chaikin Money Flow) reveals nuanced dynamics of buying and selling pressure.
Number of Combinations
With 13 tools available, the indicator supports "two to the power of thirteen minus one," which equals 8,191possible combinations. This flexibility empowers traders to experiment with various subsets of indicators, tailoring their analysis to specific market conditions or strategies.
Detailed Breakdown of Indicators
The Volume and Price, EMA Hierarchy Scoring Indicator integrates 13 widely used technical indicators, each bringing a unique perspective to market analysis. These indicators are scored individually using the EMA hierarchy system and can also be combined for more comprehensive insights.
Here’s a detailed look at what each indicator contributes:
Price Analysis
Arnaud Legoux Moving Average (ALMA):
Purpose:
ALMA smooths price data, reducing noise while maintaining responsiveness to trends.
Unique Features:
The EMA hierarchy scoring highlights how well ALMA’s EMAs align, revealing the strength of price trends.
Visualization includes a histogram of ALMA scores, a smoothed line, and a signal line.
Settings:
Adjustable parameters for the window size, offset, and sigma.
Tooltips guide users on how each setting affects the calculation.
Application:
Evaluate price momentum or combine with volume-based indicators to validate trends.
2. Price Hierarchy Score (PRC):
Purpose:
Focuses solely on price behavior to identify consistency and strength.
Visualization:
Includes a histogram representing raw scores and smoothed and signal lines for trend detection.
Settings:
Adjustable EMA periods derived from prime numbers.
Customizable smoothing and signal periods.
Volume Insights
3. Chaikin Money Flow (CMF):
Purpose:
Integrates price and volume data to measure capital flow direction and strength.
Visualization:
Raw CMF hierarchy scores are plotted, alongside smoothed and signal lines for easier trend identification.
Settings:
Lookback period adjustment for CMF calculation.
Toggle for enabling/disabling the module.
Application:
Use alongside Delta Volume to assess buying and selling pressure.
Above chart snapshot, in addition to the well-known CMF indicator, the Volume and Price indicator and the EMA Hierarchy Scoring can also be seen in the chart. By enabling the CMF evaluation, you can observe both how the CMF is analyzed and how it aligns with the price chart.
4. Delta Volume:
Purpose:
Captures the balance between buying and selling activity in the market.
Visualization:
A histogram represents the raw divergence in buying and selling strength.
Signal lines help identify momentum shifts.
Settings:
Options to set lower timeframes for more granular analysis.
Adjustable smoothing and signal periods.
Application:
Combine with CMF for a deeper understanding of capital flow dynamics.
In the above chart, alongside the Volume Delta indicator, you can observe our evaluation of this indicator's performance.
In the above chart, as explained, you can observe the impact of our evaluation metrics both individually and in combination with other indicators. This chart featuring VPR (Volume and Price Indicator along with EMA Hierarchy Scoring) illustrates the interplay between CMF and Volume Delta.
5. Volume Hierarchy Score (VOL):
Purpose:
Tracks raw volume data to identify areas of heightened market activity.
Visualization:
Histogram and smoothed lines highlight volume trends.
Settings:
Prime-numbered EMA periods to analyze volume hierarchy.
Adjustable smoothing and signal line parameters.
In the above chart, as previously explained, by analyzing the EMA of volume data over 25 iterations within specified periods (based on the first 25 prime numbers), you can observe the relationship between volume and price. We are witnessing a price increase, while the current volume position shows significant deviation and instability relative to the EMAs calculated over 25 different time periods.
In the above chart, by simultaneously enabling the evaluation of both volume and price, you can clearly observe the interplay and impact of volume and price in relation to each other.
Momentum and Trend Strength
6. Aroon Up:
Purpose:
Evaluates the strength of trends by measuring time since price highs.
Visualization :
Hierarchy scores plotted as histograms with trend-tracking smoothed and signal lines.
Settings:
Lookback period adjustments.
Module toggle for focus on Aroon trends.
If the analysis and interpretation of Aroon lines seem somewhat complex, the Volume and Price Indicator along with EMA Hierarchy Scoring provides a clear and intuitive representation of the Aroon indicator in relation to the price chart, as you can see in the current chart.
7. Average Directional Index (ADX):
Purpose:
Quantifies the strength of trends, regardless of direction.
Visualization:
ADX scores and smoothed lines for trend confirmation.
Settings:
Adjustable directional indicator (DI) and ADX smoothing periods.
Tooltip guidance for parameter optimization.
The simultaneous chart of the well-known ADX indicator alongside the evaluation system of the Volume and Price Indicator with EMA Hierarchy Scoring provides an integrated perspective on the ADX indicator.
8. Elder Force Index (EFI):
Purpose:
Combines price and volume to measure the strength of price movements.
Visualization:
EFI hierarchy scores with clear trend representation through signal and smoothed lines.
Settings:
Length adjustments for sensitivity control.
Smoothing and signal line customization.
In the above chart, we simultaneously have the well-known EFI indicator and the Volume and Price Indicator along with EMA Hierarchy Scoring. As we progress further, you will become increasingly familiar with the functionality and precision of the Volume and Price Indicator along with EMA Hierarchy Scoring.
Volatility and Oscillators
9. Ehler Fisher Transform:
Purpose:
Highlights extreme price movements by transforming price data into a Gaussian distribution.
Visualization:
Fisher Transform scores with smoothed trend indicators.
Settings:
Fisher length adjustment.
Module toggle and smoothing controls.
10. McGinley Dynamic (MGD):
Purpose:
Tracks price trends while adjusting for volatility, providing a smoother signal.
Visualization:
Raw MGD hierarchy scores with smoothed and signal lines.
Settings:
Lookback period customization.
Adjustable smoothing and signal periods
.
Ichimoku Components
11. Conversion Line (ICMC):
Purpose:
Captures short-term price equilibrium levels within the Ichimoku framework.
Visualization:
Short-term hierarchy scores visualized with smoothed lines.
Settings:
Adjustable conversion line length.
Tooltips explaining Ichimoku-related insights.
12. Base Line (ICMB):
Purpose:
Identifies medium-term equilibrium levels in the Ichimoku system.
Visualization:
Scores and smoothed trend lines for medium-term trends.
Settings:
Base line period adjustments.
Tooltip guidance for Ichimoku analysis.
In the chart below, to better illustrate the capabilities of the Volume and Price, EMA Hierarchy Scoring relation, we present a chart that evaluates the simultaneous interaction of Ichimoku Base and Conversion lines, Price, Volume, and Delta Volume.
Market Health
13. Money Flow Index (MFI):
Purpose:
Detects overbought or oversold conditions using price and volume data.
Visualization:
MFI hierarchy scores with trend tracking through smoothed and signal lines.
Settings:
Lookback period customization for sensitivity adjustment.
Module toggle and visualization controls.
EMA of Indicators: A Unified Scoring Metric
The EMA of Indicators module introduces a unique way to aggregate and analyze the individual scores of all 13 indicators. By applying a unified EMA calculation to their hierarchy scores, this module provides a single, combined metric that reflects the overall market sentiment based on the collective behavior of all indicators.
How It Works
1. Indicator-Specific EMAs:
An EMA is calculated for each of the 13 indicator hierarchy scores. The EMA period is adjustable in the settings menu, allowing traders to control how responsive the metric is to recent changes.
2. Combined EMA Calculation:
The individual EMAs are summed and averaged to generate a single Combined EMA Value. This value represents the average performance and alignment of all the indicators.
3. Smoothed and Signal Lines:
To enhance the interpretability of the Combined EMA Value:
- A Smoothed EMA is calculated using an additional EMA to filter out short-term fluctuations.
- A Signal Line is applied to the Smoothed EMA, providing actionable signals when crossovers occur.
Visualization
The Combined EMA Value is visualized as:
Histogram Bars: Represent the raw Combined EMA Value, highlighting positive or negative market alignment.
Smoothed Line: Tracks longer-term trends by smoothing the combined value.
Signal Line: Marks potential shifts in market sentiment when it crosses the Smoothed Line.
Customization and Settings
The settings menu allows full control over the EMA calculation:
Enable/Disable Module: Toggle the entire EMA of Indicators functionality.
Adjust EMA Period: Define the responsiveness of the individual indicator EMAs.
Set Smoothing Period: Control the degree of smoothing applied to the combined score.
Signal Line Period: Fine-tune the signal line's sensitivity for detecting trend shifts.
Tooltips accompany each parameter, ensuring that users understand their impact on the final visualization.
Applications in Market Analysis
1. Market Health Overview:
Use the Combined EMA Value as a quick snapshot of overall market sentiment based on all 13 indicators.
2. Trend Confirmation:
Analyze crossovers between the Smoothed EMA and Signal Line to confirm market trends or reversals.
3. Flexible Strategy Development:
Adjust EMA and smoothing periods to align the module with short-term or long-term trading strategies.
From EMA Scoring to Divergence-Weighted Insights
While the EMA scoring system provides insights into individual indicators and their trends, the Divergence-Weighted Volatility Adjusted Score takes this analysis further by combining and comparing all 13 indicators into a unified metric.
The Divergence-Weighted Volatility Adjusted Score
This score evaluates how the EMA scores of the 13 indicators interact and diverge, adding a layer of context and collective behavior analysis to the raw hierarchy scores.
1. Normalization:
All EMA scores are scaled to a common range, ensuring comparability regardless of the magnitude of individual indicators.
2. Divergence Analysis:
The system calculates the average score of the 13 indicators and evaluates the deviation (or divergence) of each individual score from this average.
Indicators with significant divergence are highlighted, as they often signal critical market dynamics.
3. Dynamic Weighting:
Indicators with greater divergence are assigned higher weights in the combined score. This ensures that outliers with meaningful signals are emphasized.
4. Volatility Adjustment:
The combined score is adjusted based on market volatility (calculated as the standard deviation of the score over a defined lookback period). This stabilizes the output, making it reliable even during turbulent market conditions.
Visualization and Customization
The Divergence-Weighted Volatility Adjusted Score is plotted as a dynamic line chart, offering a clear visual summary of the collective behavior of all indicators. The chart includes:
Smoothed Score Line: Filters out noise and emphasizes longer-term trends.
Signal Line: Helps identify potential trend shifts by tracking smoothed score crossovers.
Settings:
Lookback Period: Defines the time frame for volatility calculation.
Smoothing Period: Controls the degree of noise reduction in the smoothed score line.
Signal Line Period: Adjusts the responsiveness of the signal line.
These settings are fully adjustable, with tooltips guiding users to understand their impact.
Applications
The Divergence-Weighted Volatility Adjusted Score has several practical applications:
1. Cross-Indicator Alignment:
Detect when multiple indicators align or diverge, signaling potential opportunities or risks.
2. Dynamic Market Insights:
Adapt to changing conditions with the volatility-adjusted scoring.
3. Trend Confirmation:
Use smoothed and signal lines to validate trends identified by individual indicators.
Conclusion
The Volume and Price, EMA Hierarchy Scoring Indicator redefines how traders analyze financial markets. By combining 13 widely used technical tools with a structured scoring system based on Exponential Moving Averages (EMAs) and prime-numbered periods, this indicator brings depth and context to market analysis.
Key features include:
Independent Analysis: Evaluate individual indicators with precise EMA hierarchy scoring to assess their alignment with market trends.
Dynamic Combinations: Explore the relationships between indicators through over 8,000 combinations to uncover nuanced interactions and patterns.
Divergence-Weighted Scoring: Compare the collective behavior of indicators using a divergence-weighted system, providing a holistic market perspective adjusted for volatility.
Customization: Enable or disable modules, adjust smoothing and signal periods, and fine-tune settings to align the indicator with specific trading strategies.
User-Friendly Visualizations: Intuitive histograms, smoothed lines, and signal lines help traders identify trends, reversals, and market alignment at a glance.
This indicator empowers traders to move beyond isolated analysis by creating meaning and context between known tools. Whether you’re a scalper seeking short-term trends or a swing trader analyzing broader market movements, the Volume and Price, EMA Hierarchy Scoring Indicator offers insights tailored to your strategy.
Disclaimer
The Volume and Price, EMA Hierarchy Scoring Indicator is a tool for technical analysis and market evaluation. While it provides structured insights into market behavior, no indicator can guarantee success or eliminate the inherent risks of trading. Market conditions are complex, and multiple factors influence price movements.
Users are advised to:
Combine this indicator with other analysis methods, such as fundamental analysis or risk management strategies.
Make informed decisions based on their own analysis, trading goals, and risk tolerance.
Trading involves significant risk, and past performance does not guarantee future results. Always consult with a financial advisor or professional before making trading decisions.
Gradient Stochastic RSI CyclesThe Gradient Stochastic RSI Cycles indicator combines several key technical concepts into one, providing a unique perspective compared to the traditional RSI (Relative Strength Index) and other indicators typically used . Here's a breakdown of the specific features that make this indicator stand out:
1. Stochastic RSI (StochRSI):
The Stochastic RSI is a momentum indicator that applies the Stochastic Oscillator formula to the RSI. While RSI alone measures overbought and oversold conditions based on the price's relative strength, StochRSI refines this by measuring the position of RSI relative to its own range over a specified period.
This approach helps identify overbought and oversold conditions more dynamically, and it can be a leading indicator compared to the traditional RSI, which may lag in certain market conditions.
2. Key Differences from Traditional RSI:
RSI (Traditional): The RSI directly compares the average gains and losses of the price over a set period (typically 14 periods). It outputs a value between 0 and 100, where values above 70 indicate overbought conditions and values below 30 suggest oversold conditions.
Stochastic RSI: Instead of being calculated from price itself, the StochRSI is derived from the RSI, which adds an additional layer of smoothness and filtering. This makes it more responsive to changes in market momentum, often producing faster signals, especially in volatile markets.
Key Advantage: The Stochastic RSI often generates more timely signals by incorporating both RSI and Stochastic Oscillator principles. This leads to clearer identification of trend reversals or continuation signals, especially in strongly trending or choppy markets.
3. Smoothing and Signal Generation:
%K and %D Smoothing: The indicator uses two key smoothing steps for generating signals: the %K line (stochastic RSI itself) and the %D line (a smoothed version of %K). These are typical of Stochastic indicators but applied to the RSI, making it more sophisticated and adaptive to market cycles.
The moving average of %K (denoted as the "MA Line") further refines the trend signals by smoothing the price action of the %K line. This allows for better trend recognition, reducing false signals in sideways markets.
Key Advantage: The added smoothing steps from the %K, %D, and MA Line help in producing less erratic signals, enabling smoother and more accurate trend-following behavior. The MA line is especially useful in filtering out noise in the Stochastic RSI.
4. Trend Direction (Bullish vs Bearish):
Bullish/Bearish Conditions: The indicator includes a clear trend identification mechanism, where the indicator is considered bullish when the %K line is above the %D line and bearish when it is below.
This distinction is visually represented with gradient colors, where the bullish condition is highlighted with a green color (often associated with upward momentum) and bearish with a red color (indicating downward pressure).
Key Advantage: By distinguishing the trend direction visually and dynamically, this feature adds a layer of market interpretation that is not present in the traditional RSI. It offers clarity in identifying bullish or bearish cycles within market movements, making it easier for traders to align their positions with prevailing market trends.
5. Gradient Colors and Visualization:
The indicator uses gradient colors to visually represent the market condition. The color changes dynamically based on whether the market is in a bullish or bearish state, providing immediate feedback to the trader on the momentum of the asset.
This color gradient approach adds a clear visual reference compared to the traditional line-based RSI indicators, where traders have to infer trend direction based on multiple readings or conditions.
Key Advantage: The color gradient not only serves as a trend indicator but also makes the signal more visually accessible and easier to interpret in real-time.
6. Threshold Levels and Overbought/Oversold Conditions:
Horizontal Lines at 15 and 85: These thresholds are used to mark oversold and overbought levels, similar to how the 30 and 70 levels function in the traditional RSI. The key difference here is that the Stochastic RSI is more sensitive to price movements, and thus these levels can be more dynamic and precise in identifying extreme market conditions.
Key Advantage: The Stochastic RSI's threshold levels offer more precise markers for overbought and oversold conditions in comparison to the RSI, providing better actionable insights during volatile market phases.
7. Gradient Fill between %K and Midline:
The indicator fills the area between the %K line and the Midline (50) based on whether the trend is bullish or bearish, with different opacities depending on the trend.
Key Advantage: This visual fill enhances the clarity of market cycles and trend phases, making it easier for traders to spot potential trend reversals or trend-following opportunities. The fill acts as a dynamic background to reinforce the current market sentiment.
Advanced Trend Following: Unlike basic RSI or Stochastic indicators, the Gradient Stochastic RSI Cycles indicator integrates trend-following principles with stochastic analysis applied to RSI, creating a powerful hybrid for capturing market momentum.
Dynamic Visual Feedback: The gradient color effect and fill based on trend direction give this indicator a unique visual aspect that makes market conditions more intuitive and easier to analyze at a glance. This is not available in traditional RSI or most common stochastic oscillators.
Enhanced Overbought/Oversold Signals: By utilizing the Stochastic RSI, this indicator offers more responsive overbought and oversold levels, often leading to earlier signals compared to the conventional RSI.
Smooth and Adaptive: The multiple smoothing steps used in the indicator (with %K, %D, and the MA line) provide a more adaptive approach to trend filtering, reducing false signals that often occur with basic indicators.
In summary, the Gradient Stochastic RSI Cycles indicator is an advanced, adaptive tool that combines RSI, Stochastic Oscillator, and moving averages to provide traders with more accurate, timely, and visually accessible market signals. Its design helps overcome many of the limitations associated with traditional RSI or stochastic-based indicators, offering a more refined analysis of price momentum.
EMA with VWAPThis indicator combines two popular technical analysis tools: the Exponential Moving Average (EMA) and the Volume Weighted Average Price (VWAP), into a single, powerful overlay on your chart. It allows you to analyze both trend direction using the EMA and institutional interest and fair value using the VWAP, all while saving valuable indicator slots on your TradingView layout.
Key Features:
- Exponential Moving Average (EMA):
- Calculates the EMA based on a user-defined Length and Source (e.g., close, open, hl2).
- Includes an optional Offset to shift the EMA line forward or backward on the chart.
- Offers a Smoothing Line feature, allowing you to further smooth the EMA using various moving average types (SMA, EMA, SMMA (RMA), WMA, VWMA) with a customizable Smoothing Length.
- EMA and Smoothing Line can be toggled on or off.
- EMA and Smoothing Line have independent offset capabilities.
Volume Weighted Average Price (VWAP):
-Calculates the VWAP, a crucial indicator that reflects the average price weighted by volume.
- Offers a wide range of Anchor Periods for resetting the VWAP calculation, including: Session, Week, Month, Quarter, Year, Decade, Century, Earnings, Dividends, and Splits.
- Includes an optional Offset to shift the VWAP line.
- Option to Hide VWAP on 1D or Above timeframes to focus on intraday analysis.
- Provides up to three customizable Standard Deviation Bands above and below the VWAP, visually representing volatility and potential support/resistance levels.
- Bands can be calculated using either "Standard Deviation" or "Percentage" methods.
- Bands can be turned on or off independently.
How to Use:
- EMA: Use the EMA to identify the overall trend direction. An upward-sloping EMA suggests an uptrend, while a downward-sloping EMA suggests a downtrend. The Smoothing Line can help confirm the EMA's trend.
- VWAP: The VWAP acts as a benchmark for the "fair" price of an asset during the selected anchor period. Prices above the VWAP may indicate bullish sentiment, while prices below may indicate bearish sentiment.
- Bands: The Standard Deviation Bands can help identify potential overbought and oversold conditions. Price reaching the upper bands might suggest overbought levels, while price reaching the lower bands might suggest oversold levels.
Customization:
- The indicator offers extensive customization through its settings:
- EMA Settings: Adjust the EMA length, source, offset, smoothing method, and smoothing length.
- VWAP Settings: Choose the VWAP anchor period, source, offset, and whether to hide it on daily or higher timeframes.
- VWAP Bands Settings: Control the visibility, multiplier, and calculation method for each of the three standard deviation bands.
Benefits:
- Consolidated Analysis: Combines two essential indicators into one, providing a comprehensive view of price action and volume.
- Saves Indicator Slots: Frees up valuable indicator slots on your TradingView chart.
- Highly Customizable: Offers a wide range of settings to tailor the indicator to your specific trading style and preferences.
- Visual Clarity: Clearly displays the EMA, VWAP, and optional bands on the chart, facilitating quick and easy analysis.
This combined EMA and VWAP indicator is a valuable tool for traders of all levels, offering a powerful and flexible way to analyze market trends and identify potential trading opportunities.
20/50 SMA Cross 200 SMAThis Pine Script code is designed to identify and visualize crossovers of two shorter-term Simple Moving Averages (SMAs), a 20-period SMA and a 50-period SMA, with a longer-term 200-period SMA on a price chart. It also includes alerts for these crossover events. Here's a breakdown:
**Purpose:**
The core idea behind this script is to detect potential trend changes. Crossovers of shorter-term moving averages over a longer-term moving average are often interpreted as bullish signals, while crossovers below are considered bearish.
**Key Components:**
1. **Moving Average Calculation:**
* `sma20 = ta.sma(close, 20)`: Calculates the 20-period SMA of the closing price.
* `sma50 = ta.sma(close, 50)`: Calculates the 50-period SMA of the closing price.
* `sma200 = ta.sma(close, 200)`: Calculates the 200-period SMA of the closing price.
2. **Crossover Detection:**
* `crossUp20 = ta.crossover(sma20, sma200)`: Returns `true` when the 20-period SMA crosses above the 200-period SMA.
* `crossDown20 = ta.crossunder(sma20, sma200)`: Returns `true` when the 20-period SMA crosses below the 200-period SMA.
* Similar logic applies for `crossUp50` and `crossDown50` with the 50-period SMA.
3. **Recent Crossover Tracking (Crucial Improvement):**
* `lookback = 7`: Defines a lookback period of 7 bars.
* `var bool hasCrossedUp20 = false`, etc.: Declares `var` (persistent) boolean variables to track if a crossover has occurred *within* the last 7 bars. This is the most important correction from previous versions.
* The logic using `ta.barssince()` is the key:
* If a crossover happens (`crossUp20` is true), the corresponding `hasCrossedUp20` is set to `true`.
* If no crossover happens on the current bar, it checks if a crossover happened within the last 7 bars using `ta.barssince(crossUp20) <= lookback`. If so, it keeps `hasCrossedUp20` as `true`. After 7 bars, it becomes `false`.
4. **Plotting Crossovers:**
* `plotshape(...)`: Plots circles on the chart to visually mark the crossovers.
* Green circles below the bars for bullish crossovers (20 and 50).
* Red circles above the bars for bearish crossovers (20 and 50).
* Different shades of green/red (green/lime, red/maroon) distinguish between 20 and 50 SMA crossovers.
5. **Plotting Moving Averages (Optional but Helpful):**
* `plot(sma20, color=color.blue, linewidth=1)`: Plots the 20-period SMA in blue.
* Similar logic for the 50-period SMA (orange) and 200-period SMA (gray).
6. **Alerts:**
* `alertcondition(...)`: Triggers alerts when crossovers occur. This is essential for real-time trading signals.
**How it Works (in Simple Terms):**
The script continuously calculates the 20, 50, and 200 SMAs. It then monitors for instances where the 20 or 50 SMA crosses the 200 SMA. When such a crossover happens, a colored circle is plotted on the chart, and an alert is triggered. The key improvement is that it remembers if a crossover occurred in the last 7 bars and continues to display the circle during that period.
**Use Case:**
Traders use this type of indicator to identify potential entry and exit points in the market. A bullish crossover (shorter SMA crossing above the longer SMA) might be a signal to buy, while a bearish crossover might be a signal to sell.
**Key Improvements over Previous Versions:**
* **Correct Lookback Implementation:** The use of `ta.barssince()` and `var` variables is the correct and efficient way to check for crossovers within a lookback period. This fixes the major flaw in earlier versions.
* **Clear Visualizations:** The use of `plotshape` with distinct colors makes it easy to distinguish between 20 and 50 SMA crossovers.
* **Alerts:** The inclusion of alerts makes the script much more practical for real-time trading.
This improved version provides a robust and useful tool for identifying and tracking SMA crossovers.
FIR Low Pass Filter Suite (FIR)The FIR Low Pass Filter Suite is an advanced signal processing indicator that applies finite impulse response (FIR) filtering techniques to price data. At its core, the indicator uses windowed-sinc filtering, which provides optimal frequency response characteristics for separating trend from noise in financial data.
The indicator offers multiple window functions including Kaiser, Kaiser-Bessel Derived (KBD), Hann, Hamming, Blackman, Triangular, and Lanczos. Each window type provides different trade-offs between main-lobe width and side-lobe attenuation, allowing users to fine-tune the frequency response characteristics of the filter. The Kaiser and KBD windows provide additional control through an alpha parameter that adjusts the shape of the window function.
A key feature is the ability to operate in either linear or logarithmic space. Logarithmic filtering can be particularly appropriate for financial data due to the multiplicative nature of price movements. The indicator includes an envelope system that can adaptively calculate bands around the filtered price using either arithmetic or geometric deviation, with separate controls for upper and lower bands to account for the asymmetric nature of market movements.
The implementation handles edge effects through proper initialization and offers both centered and forward-only filtering modes. Centered mode provides zero phase distortion but introduces lag, while forward-only mode operates causally with no lag but introduces some phase distortion. All calculations are performed using vectorized operations for efficiency, with carefully designed state management to handle the filter's warm-up period.
Visual feedback is provided through customizable color gradients that can reflect the current trend direction, with optional glow effects and background fills to enhance visibility. The indicator maintains high numerical precision throughout its calculations while providing smooth, artifact-free output suitable for both analysis and visualization.
3_SMA_Strategy_V-Singhal by ParthibIndicator Name: 3_SMA_Strategy_V-Singhal by Parthib
Description:
The 3_SMA_Strategy_V-Singhal by Parthib is a dynamic trend-following strategy that combines three key simple moving averages (SMA) — SMA 20, SMA 50, and SMA 200 — to generate buy and sell signals. This strategy uses these SMAs to capture and follow market trends, helping traders identify optimal entry (buy) and exit (sell) points. Additionally, the strategy highlights the closing price (CP), which plays a critical role in confirming buy and sell signals.
The strategy also features a Second Buy Signal triggered if the price falls more than 10% after an initial buy signal, providing a re-entry opportunity with a different visual highlight for the second buy signal.
Features:
Three Simple Moving Averages (SMA):
SMA 20: Short-term moving average reflecting immediate market trends.
SMA 50: Medium-term moving average showing the prevailing trend.
SMA 200: Long-term moving average that indicates the overall market trend.
Buy Signal (B1):
Triggered when:
SMA 200 > SMA 50 > SMA 20, indicating a bullish market structure.
The closing price is positioned below all three SMAs, confirming a potential upward reversal.
A green label appears at the low of the bar with the text B1-Price, indicating the price at which the buy signal is generated.
Second Buy Signal (B2):
Triggered if the price falls more than 10% after the first buy signal, providing an opportunity to re-enter the market at a potentially better price.
A blue label appears at the low of the bar with the text B2-Price, showing the price at which the second buy opportunity arises.
Sell Signal (S):
Triggered when:
SMA 20 > SMA 50 > SMA 200, indicating a bearish trend.
The closing price (CP) is positioned above all three SMAs, confirming a potential downward movement.
A red label appears at the high of the bar with the text S-Price, showing the price at which the sell signal is triggered.
How It Works:
Buy Conditions:
SMA 200 > SMA 50 > SMA 20: Indicates a bullish market where the long-term trend (SMA 200) is above the medium-term (SMA 50), and the medium-term trend is above the short-term (SMA 20).
Closing price below all three SMAs: Confirms that the price is in a favorable position for a potential upward reversal.
Sell Conditions:
SMA 20 > SMA 50 > SMA 200: This setup indicates a bearish trend.
Closing price above all three SMAs: Confirms that the price is in a favorable position for a potential downward movement.
Second Buy Signal (B2): If the price falls more than 10% after the first buy signal, the strategy triggers a second buy opportunity (B2) at a potentially better price. This helps traders take advantage of pullbacks or corrections after an initial favorable entry.
Labeling System:
B1-Price: The first buy signal label, appearing when the market is bullish and the closing price is below all three SMAs.
B2-Price: The second buy signal label, triggered if the price falls more than 10% after the initial buy signal.
S-Price: The sell signal label, appearing when the market turns bearish and the closing price is above all three SMAs.
How to Use:
Add the Indicator: Add "3_SMA_Strategy_V-Singhal by Parthib" to your chart on TradingView.
Interpret Buy Signals (B1): Look for green labels with the text "B1-Price" when the closing price (CP) is below all three SMAs and the trend is bullish.
Interpret Second Buy Signals (B2): If the price falls more than 10% after the first buy, look for blue labels with "B2-Price" and a re-entry opportunity.
Interpret Sell Signals (S): Look for red labels with the text "S-Price" when the market turns bearish, and the closing price (CP) is above all three SMAs.
Conclusion:
The 3_SMA_Strategy_V-Singhal by Parthib is an efficient and simple trend-following tool for traders looking to make informed buy and sell decisions. By combining the power of three SMAs and the closing price (CP) confirmation, this strategy helps traders to buy when the market shows a strong bullish setup and sell when the trend turns bearish. Additionally, the second buy signal feature ensures that traders don’t miss out on re-entry opportunities after price corrections, giving them a chance to re-enter the market at a favorable price.
EMA Cloud Matrix with Trend Tablethis script builds upon a standard exponential moving average (ema) by adding volatility-based dynamic bands and persistent trend detection. it also enhances decision-making by including visual indicators (labels and clouds), a multi-timeframe trend table, and optional retest signals. here's an in-depth explanation:
volatility-based bands:
instead of just plotting an ema line, this script creates an upper and lower band around the ema using the average volatility (calculated as the average range of high-low over 100 bars).
the bands represent areas where price is likely to deviate significantly from the ema, signaling potential trend shifts.
persistent trend detection:
a persistent trend variable updates when price crosses above the upper band (bullish trend) or below the lower band (bearish trend). this ensures that the trend state persists until a new cross event occurs.
normal emas don't store such states—they merely provide a lagging representation of price.
visual enhancements:
a color-coded cloud dynamically highlights the area between the ema and the current trend line (upper or lower band), making trend direction clearer.
labels mark significant crossover or crossunder events, serving as potential buy or sell signals.
multi-timeframe trend table:
the table shows the trend direction (buy/sell) for the 15-minute, 4-hour, and daily timeframes, giving a broader perspective for trading decisions.
optional retest signals:
when enabled, it identifies situations where price tests the ema after trending away, providing additional opportunities for entries or exits.
first time ever - why use this and how?
why use this?
this is ideal for traders who:
struggle with trend-following strategies that lack clear entry/exit rules.
want a hybrid system combining ema-based smoothness with volatility-based adaptability.
need to visualize trends in multiple timeframes without switching charts.
how to use this?
buy signal: when the price crosses above the upper band, the trend flips to bullish. you’ll see a green upward arrow (▲) on the chart, indicating a potential long entry.
sell signal: when the price crosses below the lower band, the trend flips to bearish. a blue downward arrow (▼) appears on the chart, signaling a potential short entry.
retest signals (optional): if the price comes back to test the ema during a trend, a retest label can guide you for a secondary entry.
exit based on risk-reward ratio (rr)
this script doesn't explicitly calculate risk-reward ratios (rr), but you can manage exits effectively using the following ideas:
set a defined stop-loss:
if entering on a buy signal (crossover above upper band), place a stop below the ema or the lower band. for short signals, use the upper band as a stop.
this ensures the stop-loss dynamically adjusts with volatility.
use rr to set targets:
decide on a risk-reward ratio like 1:2 or 1:3. for example:
if your stop-loss is 20 points below your entry, set your target 40 or 60 points above for a 1:2 or 1:3 rr.
you can use trailing stops to lock in profits as the trend continues.
exit on opposite signal:
if the trend changes (e.g., price crosses below the lower band in a bullish trade), close the position.
how it gives signals and when to buy or sell
signal logic:
buy signal (bullish crossover):
when the price crosses above the upper band, the script marks it as a bullish trend and plots a green arrow (▲).
sell signal (bearish crossunder):
when the price crosses below the lower band, the script identifies it as a bearish trend and plots a blue arrow (▼).
trend continuation:
the trend state persists until the opposite condition occurs, helping you avoid noise or whipsaws.
multi-timeframe insights:
consult the trend table for confirmation across timeframes. for example:
if the 15-minute and 4-hour timeframes align with a buy trend, it strengthens the case for a long trade.
conflicting signals might suggest waiting for further confirmation.
using retest signals:
during strong trends, price often revisits the ema before resuming. if the optional retest signals are enabled, you’ll see labels at these points. they can be used to:
add to an existing position.
enter a trade if you missed the initial breakout.
key event: price crosses above the upper band
when the price closes above the upper band (ema + volatility buffer), the script identifies a bullish trend.
a green upward arrow (▲) is plotted on the chart, signaling the beginning of a long trend.
visual confirmation:
the cloud between the ema and the trend line (lower band) is filled with a light green color, representing a bullish phase.
the trend table will display "buy" with an upward arrow for the respective timeframe(s).
actionable insight:
entry: take a long position when the green ▲ appears, confirming a bullish crossover.
continuation trades: use the optional retest signals to identify pullbacks to the ema as opportunities to add to the long position.
exit: close the position when a bearish crossunder (sell signal) occurs.
identifying short trends (sell signal)
key event: price crosses below the lower band
when the price closes below the lower band (ema - volatility buffer), the script identifies a bearish trend.
a blue downward arrow (▼) is plotted on the chart, signaling the beginning of a short trend.
visual confirmation:
the cloud between the ema and the trend line (upper band) is filled with a light blue color, representing a bearish phase.
the trend table will display "sell" with a downward arrow for the respective timeframe(s).
actionable insight:
entry: take a short position when the blue ▼ appears, confirming a bearish crossunder.
continuation trades: use the optional retest signals to identify rallies back to the ema as opportunities to add to the short position.
exit: close the position when a bullish crossover (buy signal) occurs.
what makes it different from other ema indicators?
dynamic volatility adaptation:
standard ema indicators only track the average price over a given period, making them susceptible to market noise in highly volatile conditions.
this script uses a volatility buffer (average true range of high-low) to create upper and lower bands around the ema, filtering out insignificant movements and focusing on meaningful breakouts.
persistent trend logic:
unlike traditional emas that simply follow price direction, this script maintains a persistent trend state until a clear crossover or crossunder occurs:
bullish trends persist above the upper band.
bearish trends persist below the lower band.
this minimizes whipsaws in choppy markets.
visual enhancements:
the trend-colored cloud (green for long trends, blue for short trends) helps you quickly identify the market’s state.
labels (▲ and ▼) mark critical entry signals, making it easier to spot potential trades.
multi-timeframe trend confirmation:
the trend table integrates higher and lower timeframes, providing a multi-timeframe perspective:
short-term (15 minutes) for active trading.
medium-term (4 hours) for swing positions.
long-term (daily) for overall trend direction.
optional retest signals:
most ema-based strategies miss the retest phase after a breakout.
this script includes an optional feature to identify pullbacks to the ema during a trend, helping traders enter or add positions at better prices.
all-in-one system:
while traditional ema indicators only show a smoothed average line, this script integrates trend detection, volatility bands, visual aids, and multi-timeframe analysis in a single tool, reducing the need for additional indicators.
summary
this script goes beyond a simple ema by incorporating trend persistence, volatility bands, and multi-timeframe analysis. buy signals occur when price crosses above the upper band, initiating a long trend, while sell signals occur when price crosses below the lower band, initiating a short trend. it stands out due to its ability to adapt to market conditions, provide clear visual cues, and avoid the noise common in standard ema-based systems.
ETH - 12HR Double Kernel Regression Strategy ETH Double Kernel Regression Strategy
This ETH -focused, 12-hour Double Kernel Regression strategy is designed to cut through market noise and guide you toward data-backed, higher-probability trades. By utilizing two kernel regression models—Fast and Slow—this approach gauges momentum shifts and confirms trends. The strategy intelligently switches between these kernels based on identifying FOMO patterns, allowing it to adapt to changing market conditions. This ensures you enter trades when the trend is genuinely gaining strength, rather than blindly "buying the dip."
Key Concepts
Fine-Tuned Since Inception:
The strategy’s logic and filters—including price thresholds, trend moving averages (MAs), and kernel confirmations—are meticulously fine-tuned to perform consistently across all market conditions. This proactive planning enables confident entries during bullish recoveries, eliminating the need to second-guess every signal.
“Buy the Rise, Sell the Dip” Logic:
Unlike the traditional mantra, this strategy waits for slow kernel confirmation before entering uptrends. When market conditions shift, it identifies optimal entry points and holds steady if the trade isn’t losing money. This reduces guesswork and helps prevent buying into false rallies.
Sell the Hype:
The crypto market is often cluttered with noise—meme coins, last-minute hype, and social media influencers. The Double Kernel Regression approach distinguishes genuine trends from hype-driven movements. When the price exceeds simple moving averages (SMAs), the fast kernel generates a sell signal. This carefully crafted strategy helps you navigate the chaotic landscape, especially during hype-driven rallies, and ensures you sell at the top.
Try It Out
Import this strategy into your TradingView platform and observe how it reacts in real-time as market conditions change. Evaluate the signals, adjust parameters if necessary, and experience firsthand how combining sound trading philosophy with a data-driven backbone can transform your trading journey.
Smart DCA Strategy (Public)INSPIRATION
While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness.
The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy.
WHY IS IT SMART?
The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA.
WHAT THIS STRATEGY IS, AND IS NOT
This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used.
You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed.
HOW IT WORKS
The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading.
HOW TO USE
Settings:
The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap.
The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results.
Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets.
In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered.
You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings.
Backtesting:
When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading.
The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost.
Live Trading:
After you've finished backtesting you can proceed with configuring your alerts for live trading.
But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on
BTCUSD
Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day
Enter that USD amount in the Blind buy size settings box
Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day
Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy
By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period.
After you have calculated the Smart buy size, you can go ahead and set up alerts on Smart DCA buy triggers.
BOT AUTOMATION
In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator.
PLANNED FUTURE DEVELOPMENTS
Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space!
MARKETS
Crypto:
This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets.
Stocks:
I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish.
Forex:
As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex.
For more info about this strategy including backtest results, please see the full description on the invite only version of this strategy named "Smart DCA Strategy"
Market StructureThis is an advanced, non-repainting Market Structure indicator that provides a robust framework for understanding market dynamics across any timeframe and instrument.
Key Features:
- Non-repainting market structure detection using swing highs/lows
- Clear identification of internal and general market structure levels
- Breakout threshold system for structure adjustments
- Integrated multi-timeframe compatibility
- Rich selection of 30+ moving average types, from basic to advanced adaptive variants
What Makes It Different:
Unlike most market structure indicators that repaint or modify past signals, this implementation uses a fixed-length lookback period to identify genuine swing points.
This means once a structure level or pivot is identified, it stays permanent - providing reliable signals for analysis and trading decisions.
The indicator combines two layers of market structure:
1. Internal Structure (lighter lines) - More sensitive to local price action
2. General Structure (darker lines) - Shows broader market context
Technical Details:
- Uses advanced pivot detection algorithm with customizable swing size
- Implements consecutive break counting for structure adjustments
- Supports both close and high/low price levels for breakout detection
- Includes offset option for better visual alignment
- Each structure break is validated against multiple conditions to prevent false signals
Offset on:
Offset off:
Moving Averages Library:
Includes comprehensive selection of moving averages, from traditional to advanced adaptive types:
- Basic: SMA, EMA, WMA, VWMA
- Advanced: KAMA, ALMA, VIDYA, FRAMA
- Specialized: Hull MA, Ehlers Filter Series
- Adaptive: JMA, RPMA, and many more
Perfect for:
- Price action analysis
- Trend direction confirmation
- Support/resistance identification
- Market structure trading strategies
- Multiple timeframe analysis
This open-source tool is designed to help traders better understand market dynamics and make more informed trading decisions. Feel free to use, modify, and enhance it for your trading needs.
3 EMA + RSI with Trail Stop [Free990] (LOW TF)This trading strategy combines three Exponential Moving Averages (EMAs) to identify trend direction, uses RSI to signal exit conditions, and applies both a fixed percentage stop-loss and a trailing stop for risk management. It aims to capture momentum when the faster EMAs cross the slower EMA, then uses RSI thresholds, time-based exits, and stops to close trades.
Short Explanation of the Logic
Trend Detection: When the 10 EMA crosses above the 20 EMA and both are above the 100 EMA (and the current price bar closes higher), it triggers a long entry signal. The reverse happens for a short (the 10 EMA crosses below the 20 EMA and both are below the 100 EMA).
RSI Exit: RSI crossing above a set threshold closes long trades; crossing below another threshold closes short trades.
Time-Based Exit: If a trade is in profit after a set number of bars, the strategy closes it.
Stop-Loss & Trailing Stop: A fixed stop-loss based on a percentage from the entry price guards against large drawdowns. A trailing stop dynamically tightens as the trade moves in favor, locking in potential gains.
Detailed Explanation of the Strategy Logic
Exponential Moving Average (EMA) Setup
Short EMA (out_a, length=10)
Medium EMA (out_b, length=20)
Long EMA (out_c, length=100)
The code calculates three separate EMAs to gauge short-term, medium-term, and longer-term trend behavior. By comparing their relative positions, the strategy infers whether the market is bullish (EMAs stacked positively) or bearish (EMAs stacked negatively).
Entry Conditions
Long Entry (entryLong): Occurs when:
The short EMA (10) crosses above the medium EMA (20).
Both EMAs (short and medium) are above the long EMA (100).
The current bar closes higher than it opened (close > open).
This suggests that momentum is shifting to the upside (short-term EMAs crossing up and price action turning bullish). If there’s an existing short position, it’s closed first before opening a new long.
Short Entry (entryShort): Occurs when:
The short EMA (10) crosses below the medium EMA (20).
Both EMAs (short and medium) are below the long EMA (100).
The current bar closes lower than it opened (close < open).
This indicates a potential shift to the downside. If there’s an existing long position, that gets closed first before opening a new short.
Exit Signals
RSI-Based Exits:
For long trades: When RSI exceeds a specified threshold (e.g., 70 by default), it triggers a long exit. RSI > short_rsi generally means overbought conditions, so the strategy exits to lock in profits or avoid a pullback.
For short trades: When RSI dips below a specified threshold (e.g., 30 by default), it triggers a short exit. RSI < long_rsi indicates oversold conditions, so the strategy closes the short to avoid a bounce.
Time-Based Exit:
If the trade has been open for xBars bars (configurable, e.g., 24 bars) and the trade is in profit (current price above entry for a long, or current price below entry for a short), the strategy closes the position. This helps lock in gains if the move takes too long or momentum stalls.
Stop-Loss Management
Fixed Stop-Loss (% Based): Each trade has a fixed stop-loss calculated as a percentage from the average entry price.
For long positions, the stop-loss is set below the entry price by a user-defined percentage (fixStopLossPerc).
For short positions, the stop-loss is set above the entry price by the same percentage.
This mechanism prevents catastrophic losses if the market moves strongly against the position.
Trailing Stop:
The strategy also sets a trail stop using trail_points (the distance in price points) and trail_offset (how quickly the stop “catches up” to price).
As the market moves in favor of the trade, the trailing stop gradually tightens, allowing profits to run while still capping potential drawdowns if the price reverses.
Order Execution Flow
When the conditions for a new position (long or short) are triggered, the strategy first checks if there’s an opposite position open. If there is, it closes that position before opening the new one (prevents going “both long and short” simultaneously).
RSI-based and time-based exits are checked on each bar. If triggered, the position is closed.
If the position remains open, the fixed stop-loss and trailing stop remain in effect until the position is exited.
Why This Combination Works
Multiple EMA Cross: Combining 10, 20, and 100 EMAs balances short-term momentum detection with a longer-term trend filter. This reduces false signals that can occur if you only look at a single crossover without considering the broader trend.
RSI Exits: RSI provides a momentum oscillator view—helpful for detecting overbought/oversold conditions, acting as an extra confirmation to exit.
Time-Based Exit: Prevents “lingering trades.” If the position is in profit but failing to advance further, it takes profit rather than risking a trend reversal.
Fixed & Trailing Stop-Loss: The fixed stop-loss is your safety net to cap worst-case losses. The trailing stop allows the strategy to lock in gains by following the trade as it moves favorably, thus maximizing profit potential while keeping risk in check.
Overall, this approach tries to capture momentum from EMA crossovers, protect profits with trailing stops, and limit risk through both a fixed percentage stop-loss and exit signals from RSI/time-based logic.
[blackcat] L1 Simple Dual Channel Breakout█ OVERVIEW
The script " L1 Simple Dual Channel Breakout" is an indicator designed to plot dual channel breakout bands and their long-term EMAs on a chart. It calculates short-term and long-term moving averages and deviations to establish upper, lower, and middle bands, which traders can use to identify potential breakout opportunities.
█ LOGICAL FRAMEWORK
Structure:
The script is structured into several main sections:
• Input Parameters: The script does not explicitly define input parameters for the user to adjust, but it uses default values for short_term_length (5) and long_term_length (181).
• Calculations: The calculate_dual_channel_breakout function performs the core calculations, including the blast condition, typical price, short-term and long-term moving averages, and dynamic moving averages.
• Plotting: The script plots the short-term bands (upper, lower, and middle) and their long-term EMAs. It also plots conditional line breaks when the short-term bands cross the long-term EMAs.
Flow of Data and Logic:
1 — The script starts by defining the calculate_dual_channel_breakout function.
2 — Inside the function, it calculates various moving averages and deviations based on the input prices and lengths.
3 — The function returns the calculated bands and EMAs.
4 — The script then calls this function with predefined lengths and plots the resulting bands and EMAs on the chart.
5 — Conditional plots are added to highlight breakouts when the short-term bands cross the long-term EMAs.
█ CUSTOM FUNCTIONS
The script defines one custom function:
• calculate_dual_channel_breakout(close_price, high_price, low_price, short_term_length, long_term_length): This function calculates the short-term and long-term bands and EMAs. It takes five parameters: close_price, high_price, low_price, short_term_length, and long_term_length. It returns an array containing the upper band, lower band, middle band, long-term upper EMA, long-term lower EMA, and long-term middle EMA.
█ KEY POINTS AND TECHNIQUES
• Typical Price Calculation: The script uses a modified typical price calculation (2 * close_price + high_price + low_price) / 4 instead of the standard (high_price + low_price + close_price) / 3.
• Short-term and Long-term Bands: The script calculates short-term bands using a simple moving average (SMA) of the typical price and long-term bands using a relative moving average (RMA) of the close price.
• Conditional Plotting: The script uses conditional plotting to highlight breakouts when the short-term bands cross the long-term EMAs, enhancing visual identification of trading signals.
• EMA for Long-term Trends: The use of Exponential Moving Averages (EMAs) for long-term bands helps in smoothing out short-term fluctuations and focusing on long-term trends.
█ EXTENDED KNOWLEDGE AND APPLICATIONS
• Modifications: Users can add input parameters to allow customization of short_term_length and long_term_length, making the indicator more flexible.
• Enhancements: The script could be extended to include alerts for breakout conditions, providing traders with real-time notifications.
• Alternative Bands: Users might experiment with different types of moving averages (e.g., WMA, HMA) for the short-term and long-term bands to see if they yield better results.
• Additional Indicators: Combining this indicator with other technical indicators (e.g., RSI, MACD) could provide a more comprehensive trading strategy.
• Backtesting: Users can backtest the strategy using Pine Script's strategy functions to evaluate its performance over historical data.
VWAP SlopeThis script calculates and displays the slope of the Volume Weighted Average Price (VWAP) . It compares the current VWAP with its value from a user-defined lookback period to determine the slope. The slope is color-coded: green for an upward trend (positive slope) and red for a downward trend (negative slope) .
Key Points:
VWAP Calculation: The script calculates the VWAP based on a user-defined timeframe (default: daily), which represents the average price weighted by volume.
Slope Determination: The slope is calculated by comparing the current VWAP to its value from a previous period, providing insight into market trends.
Color-Coding: The slope line is color-coded to visually indicate the market direction: green for uptrend and red for downtrend.
This script helps traders identify the direction of the market based on VWAP , offering a clear view of trends and potential turning points.
VWAP - TrendThis Pine Script calculates the Volume Weighted Average Price (VWAP) for a specified timeframe and plots its Linear Regression over a user-defined lookback period . The regression line is color-coded: green indicates an uptrend and red indicates a downtrend. The line is broken at the end of each day to prevent it from extending into the next day, ensuring clarity on a daily basis.
Key Features:
VWAP Calculation: The VWAP is calculated based on a selected timeframe, providing a smoothed average price considering volume.
Linear Regression: The script calculates a linear regression of the VWAP over a custom lookback period to capture the underlying trend.
Color-Coding: The regression line is color-coded to easily identify trends—green for an uptrend and red for a downtrend.
Day-End Break: The regression line breaks at the end of each day to prevent continuous plotting across days, which helps keep the analysis focused within daily intervals.
User Inputs: The user can adjust the VWAP timeframe and the linear regression lookback period to tailor the indicator to their preferences.
This script provides a visual representation of the VWAP trend, helping traders identify potential market directions and turning points based on the linear regression of the VWAP.
Moving Average Cross; Linear RegressionThis Pine Script is designed to display smoothed linear regression lines on a chart, with an option to adjust the regression period lengths and smoothing factor. The script calculates short-term and long-term linear regression lines based on the selected timeframe. These regression lines act as a regressed moving average cross , visually representing the interaction between the two smoothed linear regressions.
Short Regression Line: A linear regression line based on a short lookback period, colored blue for an uptrend and orange for a downtrend .
Long Regression Line: A linear regression line based on a longer lookback period, similarly colored blue for an uptrend and orange for a downtrend .
The script provides input options to adjust:
The length of short and long regression periods.
The smoothing length for the regression lines.
The timeframe for the linear regression calculations.
This tool can help traders observe the crossovers between the two smoothed linear regression lines, which are similar to moving average crossovers, but with the added benefit of regression-based smoothing to reduce noise. The color-coding allows for easy trend identification, with blue indicating an uptrend and orange indicating a downtrend.
DemaRSI StrategyThis is a repost to a old script that cant be updated anymore, the request was made on Feb, 27, 2016.
Here's a engaging description for the tradingview script:
**DemaRSI Strategy: A Proven Trading System**
Join thousands of traders who have already experienced the power of this highly effective strategy. The DemaRSI system combines two powerful indicators - DEMA (Double Exponential Moving Average) and RSI (Relative Strength Index) - to generate profitable trades with minimal risk.
**Key Features:**
* **Trend-Following**: Our algorithm identifies strong trends using a combination of DEMA and RSI, allowing you to ride the waves of market momentum.
* **Risk Management**: The system includes built-in stop-loss and take-profit levels, ensuring that your gains are protected and losses are minimized.
* **Session-Based Trading**: Trade during specific sessions only (e.g., London or New York) for even more targeted results.
* **Customizable Settings**: Adjust the length of moving averages, RSI periods, and other parameters to suit your trading style.
**What You'll Get:**
* A comprehensive strategy that can be used with any broker or platform
* Easy-to-use interface with customizable settings
* Real-time performance metrics and backtesting capabilities
**Start Trading Like a Pro Today!**
This script is designed for intermediate to advanced traders who want to take their trading game to the next level. With its robust risk management features, this strategy can help you achieve consistent profits in various market conditions.
**Disclaimer:** This script is not intended as investment advice and should be used at your own discretion. Trading carries inherent risks, and losses are possible.
~Llama3
MicuRobert EMA Cross StrategyThis is a repost of a old strategy that cant be updated anymore, it was a request for a user made in Oct, 6, 2015
Here's a possible engaging description for the tradingview script:
**MicuRobert EMA Cross V2: A Powerful Trading Strategy**
Join the ranks of successful traders with this advanced strategy, designed to help you profit from market trends. The MicuRobert EMA Cross V2 combines two essential indicators - Exponential Moving Average (EMA) and Divergence EMA (DEMA) - to generate buy and sell signals.
**Key Features:**
* **Trading Session Filter**: Only trade during your preferred session, ensuring you're in sync with market conditions.
* **Trailing Stop**: Automatically adjust stop-loss levels to lock in profits or limit losses.
* **Customizable Trade Size**: Set the size of each trade based on your risk tolerance and trading goals.
**How it Works:**
The script uses two EMAs (5-period and 34-period) to identify trends. When the shorter EMA crosses above the longer one, a buy signal is generated. Conversely, when the shorter EMA falls below the longer one, a sell signal is triggered. The strategy also incorporates divergence analysis between price action and the EMAs.
**Visual Aids:**
* **EMA Plots**: Visualize the two EMAs on your chart to gauge market momentum.
* **Buy/Sell Signals**: See when buy or sell signals are generated, along with their corresponding entry prices.
* **Trailing Stop Lines**: Monitor stop-loss levels as they adjust based on price action.
**Get Started:**
Download this script and start trading like a pro! With its robust features and customizable settings, the MicuRobert EMA Cross V2 is an excellent addition to any trader's arsenal.
~Llama3
AuriumFlowAURIUM (GOLD-Weighted Average with Fractal Dynamics)
Aurium is a cutting-edge indicator that blends volume-weighted moving averages (VWMA), fractal geometry, and Fibonacci-inspired calculations to deliver a precise and holistic view of market trends. By dynamically adjusting to price and volume, Aurium uncovers key levels of confluence for trend reversals and continuations, making it a powerful tool for traders.
Key Features:
Dynamic Trendline (GOLD):
The central trendline is a weighted moving average based on price and volume, tuned using Fibonacci-based fast (34) and slow (144) exponential moving average lengths. This ensures the trendline adapts seamlessly to the flow of market dynamics.
Formula:
GOLD = VWMA(34) * Volume Factor + VWMA(144) * (1 - Volume Factor)
Fractal Highs and Lows:
Detects pivotal market points using a fractal lookback period (default 5, odd-numbered). Fractals identify local highs and lows over a defined window, capturing the structure of market cycles.
Trend Background Highlighting:
Bullish Zone: Price above the GOLD line with a green background.
Bearish Zone: Price below the GOLD line with a red background.
Buy and Sell Alerts:
Generates actionable signals when fractals align with GOLD. Bullish fractals confirm continuation or reversal in an uptrend, while bearish fractals validate a downtrend.
The Math Behind Aurium:
Volume-Weighted Adjustments:
By integrating volume into the calculation, Aurium dynamically emphasizes price levels with greater participation, giving traders insight into zones of institutional interest.
Formula:
VWMA = EMA(Close * Volume) / EMA(Volume)
Fractal Calculations:
Fractals are identified as local maxima (highs) or minima (lows) based on the surrounding bars, leveraging the natural symmetry in price behavior.
Fibonacci Relationships:
The 34 and 144 EMA lengths are Fibonacci numbers, offering a natural alignment with price cycles and market rhythms.
Ideal For:
Traders seeking a precise and intuitive indicator for aligning with trends and detecting reversals.
Strategies inspired by Bill Williams, with added volume and fractal-based insights.
Short-term scalpers and long-term trend-followers alike.
Unlock deeper market insights and trade with precision using Aurium!
Trend Strength/DirectionThis is a really good, though complex indicator, so I will add two different explanations so to appease both the laymen and those who take the time to read thoroughly.
Simple Explanation
This indicator utilizes 6HMA's to display their angles
The greater the angle ---> the stronger the trend
If more angles are positive, then trend is very strong
If more are negative, then very negative
Comprehensive Explanation
6 angles, each of a different time frame are used to represent direction and trend strength. Angles are used because they intrinsically represent momentum and speed. An angle of 45 represents a perfect balance between something that can cover the furthest distance without compensating for speed. 1 of the 6 angles is intended(though customizable) to represent the 5 hma's angle. This is because the 5hma is very good at representing very near term price action.
Angle Levels
Its important to understand what the angle levels mean for the underlying hma's. The 0 level represents a hma that is horizontal. This is important because this is the point at which it decides to be bullish or bearish. +/- 45, as noted before, represent bullishness/bearishness that represent strong trends without compensating for speed. A continuous increase/decrease and or a cross of these levels generally indicate significant change in sentiment, of which trades may be taken.
Strategy
You should weigh your decision by those angles that represent the longer time frame. If more angles represent a certain sentiment, it is obviously unwise to fight against that long term sentiment. The purpose of this indicator was to provide a proper representation of trend direction and strength, but also solve the problem of when you should 'dip' buy.
For an example: if all angles are increase or decreasing, then you may use the 5hma's angle to find the proper points at which you will enter a position.
***NOTE: I dont think the +/- 45 bands should indicate 'overbought' or 'oversold' zones that some might assume. Instead you should wait for a crossing of this zone.
Adaptive MAAdaptive Moving Average (AMA)
Overview
The Adaptive Moving Average (AMA) script is designed to calculate and plot a moving average that adapts dynamically based on market conditions. This script uses pivot-based periods for its calculation, allowing it to adjust its behavior in response to market volatility and trends. It supports both Simple Moving Average (SMA) and Exponential Moving Average (EMA).
Features
Dynamic Period Calculation: Leverages the DynamicPeriodPublic library to compute periods based on pivot points, providing an adaptive length for the moving average.
Customizable Parameters: Users can choose predefined "Fast" and "Slow" settings or manually configure the parameters for greater control.
Supports SMA and EMA: Flexibility to choose between SMA and EMA for the moving average calculation.
Inputs
Source ( src ): Data source for the moving average (e.g., close price).
Default: close
Length Type ( length_type ): Determines the type of period calculation.
Options: Fast, Slow, Manual
MA Type ( ma_type ): Specifies the type of moving average to calculate.
Options: SMA, EMA
Manual Parameters (used when length_type is set to Manual):
Left Bars ( left_bars ): Number of left-hand bars for pivot detection.
Right Bars ( right_bars ): Number of right-hand bars for pivot detection.
Number of Pivots ( num_pivots ): Minimum number of pivots for dynamic period calculation.
Length Multiplier ( length_mult ): Multiplier applied to the calculated period.
Use Cases
Trend Analysis: Identify market trends with an average that adapts to changing conditions.
Volatility-Based Strategies: Adjust strategies dynamically in response to market volatility.
Custom Configurations: Fine-tune pivot parameters for specific markets or assets using the "Manual" mode.
Example Usage
Select the desired length type (Fast, Slow, or Manual).
If Manual is selected, configure the pivot detection parameters and length multiplier.
Choose the moving average type (SMA or EMA).
Observe the adaptive moving average plotted on the chart.
